Cognitive Robotics

In this article I aim to provide a comprehensive introduction to the field of cognitive robotics by providing you with some definitions, examples, links to information resources, courses, and research projects. Also, the research motivations of this field are discussed, as well as main application areas and the inspiration in natural cognitive systems.

The field of Cognitive Robotics is very much related with Machine Consciousness (MC). Indeed, I consider MC as a subfield or a specific focus of the research on Cognitive Robotics. Any implementation of the functionality of consciousness has to be framed within a cognitive architecture. Consciousness per se does not make any sense unless it is integrated in a subject able to develop end to end (embodied) processes like perception and behavior.

The ultimate aim of the development of cognitive architectures is the implementation of machines that are able to “know what they are doing”, thus being more robust, adaptive, and flexible. Social robots are significant example of the kind of applications that cognitive robots (and particularly conscious robots) might perform. Interacting with humans is an extremely complex task where all these cognitive capabilities are required.

Future cognitive robots are expected to be able to interact with humans, acting and learning in unpredictable environments.

Introduction to Cognitive Robotics (excerpt taken from [0])

Research in robotics has traditionally emphasized low-level sensing and control tasks including sensory processing, path planning, and manipulator design and control. In contrast, research in cognitive robotics is concerned with endowing robots and software agents with higher level cognitive functions that enable them to reason, act and perceive in changing, incompletely known, and unpredictable environments in a robust manner. Such robots must, for example, be able to reason about goals, actions, resources (linear and/or non-linear, discrete and/or continuous, replinishable or expendable), when to perceive and what to look for, the cognitive states of other agents, time, collaborative task execution, etc. In short, cognitive robotics is concerned with integrating reasoning, perception and action with a uniform theoretical and implementation framework.

The use of both software robots (softbots) and robotic artifacts in everyday life is on the upswing and we are seeing increasingly more examples of their use in society with commercial products around the corner and some already on the market. As interaction with humans increases, so does the demand for sophisticated robotic capabilities associated with deliberation and high-level cognitive functions. Combining results from the traditional robotics discipline with those from AI and cognitive science has and will continue to be central to research in cognitive robotics.

Research Motivation

Current advances in robotics provide us with a variety of autonomous and powerful mechanical devices, ranging from manufacturing robots to (currently very limited) automated domestic assistants. However, the full range of potential applications of such machines cannot be achieved without a cognitive-like control system. One possible strategy in the development of the control software of more advanced robots is to base the design in cognitive-inspired architectures.

Classical software engineering techniques and AI approaches cannot entirely cope with the great complexity of processes like perception and behavior. The design of artificial cognition architectures might improve the intelligent (and socially-situated) performance of next-generation robots.

Application Areas

Cognitive robots application areas could be those in which cognitive capabilities are required, like interaction with humans or just real life environments (in opposition to ideal controlled manufacturing environments).

Research in cognitive sciences and neuroscience is a valuable source of inspiration in the design of artificial cognitive-like systems. Usually, the work on cognitive robotics is based on principles and findings that come from cognitive psychologists and neurobiologists. Aspects like memory and its underlying neural mechanics can be tried to be imitated in robot’s artificial brains. However, some of the most classical algorithms used in robotics don’t take into account these cognitive concepts. One reason could be that in controlled environments classical algorithms work best or good enough. The application of cognitive models in robotics is a relatively young field; therefore, a lot of research can still be done in this area.

There exist several approaches in the application of human cognitive models to artificial machines. For instance, system-level brain modeling considers functional areas of the brain and their interaction (as in Ikaros [7]).